Analysis of the degree of importance of information using newspapers and questionnaires

Our objective is to estimate and clarify the factors that determine the degree of importance of information by extracting the words that characterize the degree of importance and to construct a system for automatically estimating this degree of importance. We studied the degree of importance of information by using machine learning. We first performed experiments using newspaper documents (D_n). In this experiment, we assumed that a document on the front page or at the top of the front page is important. We were able to identify important documents with a precision of 0.9 by using machine learning. We found that in the case of a newspaper, the degree of importance can be estimated with high precision. Next, to estimate the degree of importance that people attach to a document, we conducted experiments using questionnaire data (D_q) as test data. In these experiments, the subjects were asked to identify which document from a pair was more important, and a high accuracy of 94% was obtained with more than 80% of them responding with the same answer. Furthermore, on using newspaper documents (D_n) as training data, we could obtain (i) the same accuracy by using D_n only instead of using D_n with D_q and (ii) a higher accuracy on using D_n and D_q instead of using D_q only. This observation is useful because preparing questionnaire data (D_q) can be an expensive process, whereas (D_n) is free. Finally, we extracted the characteristic words that differentiated important information from less important information by calculating the parameters of the features in machine learning (maximum entropy (ME) method).